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import gradio as gr
from transformers import pipeline
# Load the model for emotion detection
classifier = pipeline(
"text-classification",
model='bhadresh-savani/distilbert-base-uncased-emotion',
return_all_scores=True
)
def detect_emotions(emotion_input):
"""
Detect emotions in the input text using a pre-trained model.
Returns a dictionary mapping emotions to their respective scores.
"""
prediction = classifier(emotion_input)
output = {emotion["label"]: round(emotion["score"], 4) for emotion in prediction[0]}
return output
examples = [
["Every song on the radio reminds me of you."],
["There's an unfamiliar shadow in the corner of the room."]
]
css = """
footer {display: none !important;}
.output-markdown {display: none !important;}
.gr-button-primary {
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: rgb(17, 20, 45) !important;
border: none !important;
text-align: center !important;
font-family: 'Poppins', sans-serif !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: none !important;
}
.gr-button-primary:hover {
background: rgb(66, 133, 244) !important;
box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
"""
interface = gr.Interface(
fn=detect_emotions,
inputs=gr.Textbox(placeholder="Enter text here", label="Input", lines=2),
outputs=gr.Label(num_top_classes=5, label="Emotion"),
title="Emotion Analysis",
description="Enter a text to detect the underlying emotions using a DistilBERT-based model.",
examples=examples,
css=css
)
if __name__ == "__main__":
interface.launch()
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